# read in age data
age <- read_csv(here::here("raw_data/age_data.csv")) %>%
clean_names()
# create day, month, year and date columns
age_by_date <- age %>%
mutate(
year = str_extract(week_ending, "^\\d{4}"),
monthday = str_extract(week_ending, "\\d{4}$"),
month = str_extract(monthday, "^\\d{2}"),
day = str_extract(monthday, "\\d{2}$"),
date = ymd(str_c(year, month, day)), .after = 1
)
age_date_clean <- age_by_date %>%
filter(!age_group == "All ages")
# create plot of admissions by age
ggplotly(age_date_clean %>%
group_by(date, age_group) %>%
summarise(avg_admissions = mean(number_admissions)) %>%
ggplot() +
aes(x = date, y = avg_admissions, colour = age_group) +
geom_line() +
labs(title = "Mean Weekly Admissions by Age Group",
x = "Date",
y = "Mean Admissions",
colour = "") +
theme_bw())
age_by_date %>%
filter(sex == "All") %>%
count()
sex_data_clean <- age_by_date %>%
filter(!sex == "All")
ggplotly(sex_data_clean %>%
group_by(date, sex) %>%
summarise(avg_admissions = mean(number_admissions)) %>%
ggplot() +
aes(x = date, y = avg_admissions, colour = sex) +
geom_line() +
labs(title = "Mean Weekly Admissions by Gender",
x = "Date",
y = "Mean Admissions",
colour = "") +
theme_bw())
`summarise()` has grouped output by 'date'. You can override using the `.groups` argument.
ggplotly(dep_date %>%
group_by(date, simd_quintile) %>%
summarise(avg_admissions = mean(number_admissions)) %>%
ggplot() +
aes(x = date, y = avg_admissions, colour = simd_quintile) +
geom_line() +
labs(title = "Average Admissions by SIMD",
x = "Date",
y = "Average Admissions",
colour = "SIMD Quintile"))
`summarise()` has grouped output by 'date'. You can override using the `.groups` argument.
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